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Research Journal of Pharmacy and Technology
Year : 2020, Volume : 13, Issue : 3
First page : ( 1368) Last page : ( 1376)
Print ISSN : 0974-3618. Online ISSN : 0974-360X.
Article DOI : 10.5958/0974-360X.2020.00252.8

Computational studies on Differential gene Expression in Malaria Microarray Dataset

Suganya Jeyabaskar, Radha Mahendran*, Hubert

Department of Bioinformatics, School of Life Sciences, VISTAS, Chennai-600117, Tamil Nadu, India.

*Corresponding Author E-mail: mahenradha@gmail.com, hodbioinfo@velsuniv.ac.in

Online published on 22 April, 2020.

Abstract

Malaria is considered to be one of the deadliest diseases among various parasitic diseases. Over the past ten decades, Plasmodium falciparum developed high drug resistance towards the existing standard drug quinine. The drug quinine is one of the natural alkaloids derived from the bark of the cinchona tree and for many centuries, it has been used as the anti-malarial drug. One of the main reasons for the development of drug-resistance in the parasite was that the drug quinine may lose its ability to inhibit the function of parasite genes which was responsible for causing 90% of the malarial disease to humans. In the present study, an in silico analysis was performed over malarial genes of Plasmodium falciparum using GEO (Gene Expression Omnibus) Databases. GEO2R tool is used to compare two or more set of experimental sample present in GEO Datasets, in order the recognize genes which show signs of the drug resistance towards the prescribed drug. Thus in the present study, the malaria micro-array datasets with the standard drug quinine were analyzed using bioinformatics tools and databases. The results retrieved from the databases predicted the genes which exhibit the drug resistance towards the standard drug quinine. Thus in silico study might provide novel clues in identifying the drug-resistant malarial genes of parasite Plasmodium falciparum.

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Keywords

Malaria, Drug Quinine, GEO Datasets, GEO2R.

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